{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "sys.path.append('/Users/iwatobipen/develop/chemoenv/psikit/psikit/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from psikit import Psikit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pk = Psikit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pk.read_from_smiles('OC(=O)c1cnccc1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer: Optimization complete!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-434.31618866070147"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pk.optimize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x113272688>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pk.mol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.58253784  0.69011262 -0.50822101 -0.21638198  0.3090496  -0.58764823\n",
      "  0.35116267 -0.29441912  0.00119611  0.40735602  0.06348114  0.06372305\n",
      "  0.15143447  0.15169252]\n",
      "[-0.60451003  0.82714117 -0.53442304 -0.24069704  0.13815438 -0.54392709\n",
      "  0.1459902  -0.21533841 -0.04066254  0.35929545  0.15532705  0.16754771\n",
      "  0.17394675  0.21215544]\n",
      "[-0.27235014  0.34136197 -0.32165514 -0.12349265  0.00183538 -0.19122938\n",
      "  0.02154698 -0.14202575 -0.0189029   0.22605628  0.10476005  0.11484402\n",
      "  0.11953793  0.13971336]\n"
     ]
    }
   ],
   "source": [
    "#caculate chages\n",
    "print(pk.calc_resp_charges())\n",
    "print(pk.calc_mulliken_charges())\n",
    "print(pk.calc_lowdin_charges())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mol = pk.mol\n",
    "atoms = mol.GetAtoms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'Symbol':[atom.GetSymbol() for atom in atoms],\n",
    "        'RESP': [float(atom.GetProp('RESP')) for atom in atoms],\n",
    "        'MULLIKEN': [float(atom.GetProp('MULLIKEN')) for atom in atoms],\n",
    "       'LOWDIN':  [float(atom.GetProp('LOWDIN')) for atom in atoms]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LOWDIN</th>\n",
       "      <th>MULLIKEN</th>\n",
       "      <th>RESP</th>\n",
       "      <th>Symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.272</td>\n",
       "      <td>-0.605</td>\n",
       "      <td>-0.583</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.341</td>\n",
       "      <td>0.827</td>\n",
       "      <td>0.690</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.322</td>\n",
       "      <td>-0.534</td>\n",
       "      <td>-0.508</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.123</td>\n",
       "      <td>-0.241</td>\n",
       "      <td>-0.216</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.309</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.191</td>\n",
       "      <td>-0.544</td>\n",
       "      <td>-0.588</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.022</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.351</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.142</td>\n",
       "      <td>-0.215</td>\n",
       "      <td>-0.294</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.019</td>\n",
       "      <td>-0.041</td>\n",
       "      <td>0.001</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.226</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.407</td>\n",
       "      <td>H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.105</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.063</td>\n",
       "      <td>H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.115</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.064</td>\n",
       "      <td>H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.120</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.151</td>\n",
       "      <td>H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.140</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.152</td>\n",
       "      <td>H</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    LOWDIN  MULLIKEN   RESP Symbol\n",
       "0   -0.272    -0.605 -0.583      O\n",
       "1    0.341     0.827  0.690      C\n",
       "2   -0.322    -0.534 -0.508      O\n",
       "3   -0.123    -0.241 -0.216      C\n",
       "4    0.002     0.138  0.309      C\n",
       "5   -0.191    -0.544 -0.588      N\n",
       "6    0.022     0.146  0.351      C\n",
       "7   -0.142    -0.215 -0.294      C\n",
       "8   -0.019    -0.041  0.001      C\n",
       "9    0.226     0.359  0.407      H\n",
       "10   0.105     0.155  0.063      H\n",
       "11   0.115     0.168  0.064      H\n",
       "12   0.120     0.174  0.151      H\n",
       "13   0.140     0.212  0.152      H"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13bf93940>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# RESP charge and MULLIKEN charge shows high correlation\n",
    "plt.scatter(df.RESP, df.MULLIKEN)\n",
    "plt.xlabel('RESP')\n",
    "plt.ylabel('MULLIKEN')\n",
    "plt.plot(np.arange(-1.0, 1.0, 0.01), np.arange(-1.0, 1.0, 0.01), c='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13c0db8d0>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.RESP, df.LOWDIN)\n",
    "plt.xlabel('RESP')\n",
    "plt.ylabel('LOWDIN')\n",
    "plt.plot(np.arange(-1.0, 1.0, 0.01), np.arange(-1.0, 1.0, 0.01), c='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13c1ef1d0>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.MULLIKEN, df.LOWDIN)\n",
    "plt.xlabel('MULLIKEN')\n",
    "plt.ylabel('LOWDIN')\n",
    "plt.plot(np.arange(-1.0, 1.0, 0.01), np.arange(-1.0, 1.0, 0.01), c='b')"
   ]
  }
 ],
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